ML & AI

Course Overview

Become an expert in machine learning and artificial intelligence through hands-on projects, model deployment, and real-world problem-solving using Python, TensorFlow, and Scikit-learn.

Duration: 24 weeks
Rating: 4.8 / 5
7,800+ Students

Detailed Syllabus

1-4: Python for Data Science & ML Foundations

  • Introduction to AI, ML, and Data Science Ecosystem
  • Python Refresher: NumPy, Pandas, Matplotlib, Seaborn
  • Data Cleaning, Transformation, and Visualization
  • Exploratory Data Analysis (EDA)
  • Feature Engineering Basics
  • Understanding Bias, Variance, and Evaluation Metrics

Tools: Python, Jupyter Notebook, Pandas, Matplotlib, Seaborn

5-8: Supervised Learning Algorithms

  • Linear and Logistic Regression from Scratch
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • k-Nearest Neighbors (k-NN)
  • Naive Bayes Classifier
  • Model Evaluation and Cross-Validation

Tools: Scikit-learn, NumPy, Matplotlib, Joblib

9-12: Unsupervised Learning & Feature Techniques

  • Clustering with K-Means, DBSCAN, and Hierarchical Models
  • Dimensionality Reduction using PCA and t-SNE
  • Association Rule Mining (Apriori, FP-Growth)
  • Anomaly Detection Techniques
  • Feature Scaling and Selection Methods
  • Applications in Customer Segmentation

Tools: Scikit-learn, Yellowbrick, Pandas, Plotly

13-16: Deep Learning & Neural Networks

  • Introduction to Artificial Neural Networks (ANN)
  • Activation Functions, Optimizers, and Backpropagation
  • Building Models using TensorFlow & Keras
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs) & LSTMs
  • Transfer Learning and Fine-Tuning Pretrained Models

Tools: TensorFlow, Keras, NumPy, Google Colab, OpenCV

17-20: Model Deployment, MLOps & Advanced Topics

  • Model Saving and Serialization (Pickle, Joblib)
  • Building APIs with Flask/FastAPI
  • Deployment on AWS, GCP, or Streamlit
  • Automated ML Pipelines with MLflow
  • Hyperparameter Tuning and Model Optimization
  • Explainable AI (SHAP, LIME)

Tools: Flask, Streamlit, MLflow, AWS/GCP, Docker

21-24: Capstone Project & AI Ethics

  • End-to-End ML Project: From Data Collection to Deployment
  • Building a Predictive or Classification Model
  • Documentation and Model Presentation
  • AI Ethics, Fairness, and Responsible AI Practices
  • Version Control with Git & GitHub
  • Final Evaluation, Presentation, and Certification

Tools: Git & GitHub, Jupyter Notebook, Streamlit / Flask, Presentation Decks

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